Gabriela Dobrița, Simona-Vasilica Oprea*, Adela Bâra
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 675-712, 2025, DOI:10.32604/cmc.2024.058656
- 03 January 2025
Abstract In the rapidly evolving landscape of television advertising, optimizing ad schedules to maximize viewer engagement and revenue has become significant. Traditional methods often operate in silos, limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing. This article introduces a novel approach that leverages Federated Learning (FL) to enhance TV ad schedule optimization, combining the strengths of local optimization techniques with the power of global Machine Learning (ML) models to uncover actionable insights without compromising data privacy. It combines linear programming for initial ads scheduling optimization with ML—specifically,… More >
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